1. Field of the Invention
The present invention relates to method for recognizing physiological sound, particularly to a method for extracting and classifying the feature of the physiological sound. The present invention also relates to a system for recognizing physiological sound.
2. Description of the Prior Arts
Analysis of heart, lung, bowel and vascular disorders by means of noninvasive auscultation has long been a very useful tool for medical diagnosis of ailments. Conventional electronic stethoscopes were not invented until 1922. Modern electronic stethoscopes can improve sound quality and provide a visual indication of heart sounds, such as phonocardiogram (PCG). PCG and electrocardiogram (ECG) are applied in the basic examination for the heart. PCG could be obtained by recording the electric signals converted from mechanical vibration (collected via placing stethoscope in certain parts of the chest) by instrument. ECG could be obtained by placing electrodes on any two places of the heart and connecting two electrodes to the positive and negative poles of ECG machine to form leads and record the ECG voltage changes of the two places in the human body. ECG could be shown in ECG paper or monitor and further reflect the rhythms of beating heart as well as the weaker parts of myocardial muscles. The pitches and occurrence time of heart sounds follow certain regular patterns. The first heart sound (S1) and the second heart sound (S2) could be observed in a normal heart. The first heart sound occurs in the contraction period of the heart, which is caused by the blood flowing into great vessels during the contraction of ventricles (ventricle contracts and both mitral valve and tricuspid valve close). The first heart sound continues for relatively longer time and with low pitch. The second heart sound occurs in the relaxation period of heart, which is caused by the vibration of ventricular wall during ventricular relaxation (aortic and pulmonary valves close and atrioventricular valve opens to allow blood flowing from atrium to ventricle). The duration of the second heart sound is shorter. Clinically, abnormal third and fourth heart sounds sometimes would be detected. The third heart sound shows low frequency and amplitude, which is caused by the vibration of ventricular wall. The fourth heart sound is caused by the vibration of ventricular wall during atrial contraction owing to blood flow rapidly entering ventricle.
Many heart diseases could be effectively diagnosed through auscultation. In some deadly heart diseases (such as heart valve dysfunction, heart failure, etc.), cardiac auscultation has already become the most successful, reliable, and inexpensive method in early diagnosis. However, the correctness of cardiac auscultation is closely related to the experiences of doctors. Also, some diseases show obvious occurrence patterns (for example, during S1 and S2 or after S2, etc.). Therefore, how to automatically detect and preliminarily judge the occurring time of S1 and S2 has already become an important issue. This issue could effectively help doctors to confirm the occurrences of diseases preliminarily. In normal situation, the time order of S1 and S2 could serve as the materials for making judgments. Nevertheless, time order is no longer reliable under the circumstances of arrhythmia. If voiceprint comparison for S1 and S2 is available, the judgment on the case of arrhythmia could be improved in quality. Researches about heart sound detection could be divided into two categories: ECG signal dependent and ECG signal independent. ECG signal dependent researches include ECG-based detections on instantaneous energy (Malarvili et al., 2003) and detection on QRS wave group and T wave (El-Segaier et al., 2005). Nonetheless, in low-quality ECG signals, it is not always possible to clearly detect T wave. Under such situation, S2 could be classified using unsupervised classifier (Carvalho et al., 2005), although such method should consider hardware equipment and the comfort of examinees. ECG-independent methods could be divided into unsupervised and supervised methods. Unsupervised methods include using normalized average Shannon energy (Liang et al., 1997) and high frequency-based methods (Kumar et al., 2006) for wavelet decomposition. Supervised methods include neural network classifier (Hebden et al., 1996) and decision making trees (Stasis et al., 1996) used for classification. In addition, the most advanced method used in recent years is to detect according to the features of the regular intervals between S1 and S2. Generally, average heart rate (Olmez et al., 2003, Kumar et al., 2006) would be assumed in research. However, such assumption is not applicable in the heart sound of arrhythmia patients.
It is relatively difficult to simultaneously and synchronously record and analyze ECG and PCG in actual clinical cases. Also, when PEA occurs, ECG cannot determine that the heart rate has stopped due to the maintenance of electrical activity. Thus, how to make diagnosis according to solely PCG became an important and mainstream research topic. Mainstream detection methods usually include the time interval features of S1 and S2. But this feature would become unreliable under the situation of arrhythmia and highly decrease the correctness of detection. Therefore, the disadvantages in prior arts should be resolved.